The rapid, world-wide expansion of cellular networks and the introduction of new wireless services combined with competition among network operators has meant an ever-increasing need for continuous improvement as to quality, capacity, and accessibility. From the network operator perspective, higher quality, increased capacity, and better accessibility must be provided while also keeping the cost of calls and other services as low as possible. Moreover, to allow for future traffic growth and the introduction of new services, major investments in network equipment and functionality are typically necessary. This new equipment and functionality must be verified in realistic circumstances before being deployed commercially. Even with equipment and functionality already in place, it is important that operators can obtain a “proof of performance” with the possibility to identify and remedy problem areas before the higher loads and/or new services exist in commercial operation.
Consequently, operators are particularly interested in obtaining information regarding the likely performance of a particular network under increased load conditions in which a greater percentage of the available radio resources is being used. For example, a network operator may want proof or demonstration regarding whether and how well an existing site configuration can provide additional service(s). But accurately providing this kind of information is difficult, particularly if there are insufficient users available to load those sites to the increased level desired and/or there is insufficient hardware currently installed at the sites to support the higher traffic load.
To test the capacity of a particular network or site configuration that includes a plurality of sites and/or sectors, the traffic could be increased by simply increasing the number of people making calls in the test area, assuming that there is sufficient installed equipment to handle the higher load. But this kind of manual loading process is time-consuming and expensive and requires that a large number of people be employed and sent out to load up the network with calls. Another problem with the manual loading approach is that it is difficult to ensure that these newly-added test users mimic the behavior of real traffic loading since they are being asked to make “artificial” test calls. Their mobility patterns and cellular phone usage may significantly differ from those of real users, thereby raising doubts about the accuracy of the system performance results so obtained.
An alternative approach to increase the effective load on the network test area would be to increase the radio resource burden of each existing user, for example, by disabling power control and/or discontinuous transmission (DTX). Since features such as power control and DTX reduce the power transmitted by each user, disabling them is equivalent to adding more users manually in terms of the traffic load level in the network. An advantage with this method over the manual loading technique is that the drawbacks regarding time, cost, organization, and accuracy of results outlined above are avoided. A disadvantage, however, is that the gain from features like power control and DTX is typically difficult to quantify in practical situations, and therefore, the effective load on the network achieved by disabling these features is uncertain.
A better loading approach to increase the effective load on the network test area is to reduce the number of available radio resources. Traffic load is typically distributed onto a limited amount or number of radio resources. For example, in the context of a radio communications network that employs time division multiple access (TDMA) technology, the radio resources include time slots and frequencies. If the same number of users may only utilize a reduced quantity of radio resources, then the load on these radio resources is increased. If the radio resources in the system, e.g., time slots and frequencies in a TDMA system, are equivalent and independent, then the performance results obtained using the reduced, sub-set of radio resources can be extrapolated to give network performance measures for the full network resource situation at higher loads.
A difficulty with reducing the amount or number of available radio resources in order to increase the effective load is that not all radio resources are equivalent and independent, which makes extrapolation of the test results uncertain. For example, frequency bandwidth is typically an important radio resource. But because multi-path fading is frequency-dependent, reducing the frequency bandwidth influences the ability of users to combat multi-path fading, which adversely affects performance. Hence, obtaining test results with a reduced frequency bandwidth suffers from the same uncertainties in extrapolation to overall network performance as those discussed above in the context of increasing the radio resource burden of each user, for example, by disabling power control and/or DTX.
In the GSM TDMA system, users typically utilize frequency bandwidth by frequency hopping over multiple frequencies, each having 200 kHz bandwidth. In a system that implements frequency hopping, like GSM, reducing the number of available radio resources might correspond to reducing the number of hopping frequencies. The result is that existing calls must be handled using the reduced number of frequencies, which increases the load on those remaining frequencies. A drawback with reducing the number of frequencies, particularly in a frequency hopping context, is that it adversely affects the ability to combat multi-path fading by reducing the frequency bandwidth used by a connection, as explained above. It also reduces the variation of radio quality within a radio block, which reduces decoding performance when significant channel coding is present, as is the case with GSM speech. Further, it reduces the interference averaging effect that allows the gains of some users, e.g. due to DTX, to benefit all users. Hence, increasing the effective load in a GSM network for test purposes by reducing the number of hopping frequencies has significant disadvantages because the radio environment experienced by the users is fundamentally altered in the process.
In a frequency hopping GSM system, the effective load on the radio resources can be measured by the frequency load, which is defined as the served traffic (the number of users and their bandwidth requirements), divided by the number of hopping frequencies times the number of time slots. Since increasing the traffic via artificially adding more “test” users, via increasing the radio resource burden of the existing users, or via reducing the number of hopping frequencies all have disadvantages, a better way of increasing the load in a GSM system is to reduce the number of time slots in each frequency or frequency hopping channel group, for example, by blocking a predetermined number of time slots to traffic. Individual time slots are independent of the other time slots, and the radio environment experienced by the users is unaffected since the correct number of hopping frequencies is still used. By carefully selecting the number of time slots to be used, the frequency load can be increased without resulting in congestion to existing users. This is achieved by either ensuring that sufficient equipment is in place to prevent congestion in the frequency hopping channel group with the reduced number of time slots, or by creating an extra frequency or frequency hopping channel group with a full set of time slots that can serve users that would otherwise be denied access to the network. In GSM, such an extra frequency channel group may typically contain a non-frequency hopping, broadcast control channel (BCCH) frequency.
This approach to increasing the effective load may be used in cellular networks that do not employ TDMA and/or frequency hopping. For example, the invention can be applied to orthogonal frequency division multiplexing (OFDM) and related access techniques by limiting the time of use for one or more sub-channel frequencies, as well as to spread spectrum, code division multiple access (CDMA) based systems. In all radio resource access techniques, time is a common radio resource. If desired, the approach can also be combined with other mechanisms/techniques for increasing effective system load, such as (but not limited to) those outlined above.
The reduced time slot approach may be used in a radio communication system that includes multiple service areas. Each service area is associated with a predetermined number or amount of radio communication resources. Each radio communication resource can be used for a prescribed period of time which can be set by one or more time units. An operator or other entity identifies a set of service areas to be tested at an increased effective load. A desired test level (e.g., increased traffic load) is determined, and a corresponding number of radio resource time units is determined to achieve the desired test level conditions. Use of radio communication resources in the determined one or more time units is blocked for a test period. Performance in each service area during the test period is determined, and thereafter, aggregated into overall test network statistics as deemed appropriate.
The performance may be determined based on measures such as: dropped call rate, received signal strength, signal quality, interference, handover success rate, and bit and block error rates. Each time unit may correspond to a time slot or a time frame, and each radio resource may be associated with a frequency, a frequency range, or a frequency hopping group. Alternatively, if the radio communication system uses code division multiple access (CDMA), each radio resource may be associated with the code, and each time unit may correspond to a transmission time interval (TTI). In that case, the radio transmitters in the set of service areas transmit at an increased power level during unblocked TTIs and at a decreased power level during blocked TTIs.
In an example embodiment, synchronous time units in all of the service areas being tested are blocked. Synchronous time units or time slots are preferentially chosen because interference from one service area typically affects performance in other service areas. If the effect of the increased effective load is to be registered equally across the network test area in the form of increased interference, the load must be concentrated to the same time unit(s) in all service areas.
In practice, the blocking of exactly synchronous time units or time slots may not be possible. This could be the case, for example, if different service areas obtain their timing reference from different and independent transmission links. In asynchronous systems, time units or time slots as close to synchronous as possible should be blocked in the different service areas. Any non-alignment can then be corrected for in the effective load calculation. Such a correction is desirable since non-alignment reduces the interference experienced by the traffic in the network, and therefore, the actual effective load achieved during the test.
In one non-limiting, example embodiment applied to a control node, a controller in that node controls plural radio base station units. The radio base station units may correspond to base station sites or to base station sectors. Once a set of service areas is specified for testing, one or more times in each service area is determined when one or more radio resources associated with each such service area will not be used. A performance measure is determined under those conditions. Another non-limiting, example embodiment is a computer program product that includes computer code operable to control a computer. That code may include first logic operable to determine a test set of service areas for testing, second logic operable to determine one or more times in each service area when one or more radio resources for each service area will not be used, and third logic operable to determine each service area's performance under those conditions.